Method of determining reference features for use in an optical object initialization tracking process and object initialization tracking method

ABSTRACT

A method of determining reference features for use in an optical object initialization tracking process is disclosed, said method comprising the following steps: a) capturing at least one current image of a real environment or synthetically generated by rendering a virtual model of a real object to be tracked with at least one camera and extracting current features from the at least one current image, b) providing reference features adapted for use in an optical object initialization tracking process, c) matching a plurality of the current features with a plurality of the reference features, d) estimating at least one parameter associated with the current image based on a number of current and reference features which were matched, and determining for each of the reference features which were matched with one of the current features whether they were correctly or incorrectly matched, e) wherein the steps a) to d) are processed iteratively multiple times, wherein in step a) of every respective iterative loop a respective new current image is captured by at least one camera and steps a) to d) are processed with respect to the respective new current image, and f) determining at least one indicator associated to reference features which were correctly matched and/or to reference features which were incorrectly matched, wherein the at least one indicator is determined depending on how often the respective reference feature has been correctly matched or incorrectly matched, respectively.

Applicant hereby claims priority benefits to U.S. Provisional PatentApplication No. 61/289,763 filed Dec. 23, 2009, and EP Patent Appln. No.09180616.6 filed Dec. 23, 2009, the disclosures of which are hereinincorporated by reference.

BACKGROUND OF THE INVENTION

1. Technical Field

The present invention relates to a method of determining referencefeatures for use in an optical object initialization tracking processand to an object initialization tracking method making use of referencefeatures, for example extracted from a reference image. Moreover, thepresent invention relates to a computer program product comprisingsoftware code sections for implementing the method according to theinvention.

2. Background Information

Augmented Reality Systems permit the superposition of computer-generatedvirtual information with visual impressions of a real environment. Tothis end, the visual impressions of the real world are mixed withvirtual information, e.g. by means of a semi-transmissive data displayworn on the head of a user. The blending-in of virtual information orobjects can be effected in context-dependent manner, i.e. matched to andderived from the respective environment viewed. As virtual information,it is basically possible to use any type of data, such as texts, imagesetc. The real environment is detected e.g. with the aid of a cameracarried on the head of the user.

When the person using an augmented reality system turns his or her head,tracking of the virtual objects with respect to the changing field ofview is necessary. The real environment may be a complex apparatus, andthe object detected can be a significant member of the apparatus. Duringa so-called tracking operation, a real object detected during an objectinitialization process may serve as a reference for computing theposition at which the virtual information is to be displayed orblended-in in an image taken up by the camera. Due to the fact that theuser may change his or her position and orientation, the real object hasto be subjected to continuous tracking in order to display the virtualinformation at the correct position in the display device also in caseof an altered position and/or altered orientation of the user. Theeffect achieved thereby is that the information, irrespective of theposition and/or orientation of the user, is displayed in the displaydevice in context-correct manner with respect to reality. An augmentedreality system in this regard is an example of the utilization of suchso-called markerless tracking systems.

Standard Tracking Initialization Approach:

When doing markerless tracking of a certain target given one or multiplereference images of that target, the standard tracking initializationframework can be described using the following steps. In this regard,FIG. 1 shows a flow diagram of an exemplary process in which the numbersof the following steps are denoted in parentheses.

Once a set of digital images (one or more images) are acquired:

-   -   1—Features are extracted from a set of these “reference” digital        images and stored. These features are commonly referred to as        “reference features” and may be denoted with where i is in {1,        2, . . . , N_(R)} and N_(R) is the number of reference features        extracted. The features can be points, a set of points (lines,        segments, regions in the image or simply a group of pixels),        etc.    -   2—Descriptors (or classifiers) may be computed for every        reference feature extracted and stored. These descriptors may be        called “reference” descriptors.        Then, when having the real target facing the camera that        captures live or so-called “current” images:    -   3—For every current image captured, features are extracted.        These features may be called “current features”.    -   4—Descriptors (or classifiers) may be computed for every current        feature extracted and stored. These descriptors may be referred        to as “current descriptors” and may be denoted with c_(j), where        j is in {1, 2, . . . , N_(C)} and N_(C) is the number of current        features extracted.    -   5—The current features are matched with the reference features        using the reference and current descriptors: if the descriptors        are relatively close in terms of a certain similarity measure,        they are matched. For example, if every descriptor is written as        a vector of numbers, when comparing two descriptors, one can use        the Euclidian distance between two corresponding vectors as        similarity measure. A match is denoted as m_(k)={r_(k),c_(k)}        where k is in {1, 2, . . . , N_(M)} and is the N_(M) number of        matched features.    -   6—Given the model of the target, an outlier rejection algorithm        is performed. The outlier rejection algorithm may be generally        based on a robust pose estimation (explained below).    -   7—Using the correct matches, the “current” pose of the camera is        computed.

Most of the approaches for feature-based tracking initialization performa robust estimation in order to remove incorrect matches. This step iscalled outlier rejection (see above Step 6). This is due to the factthat whatever descriptor or classifier used there is no way to avoidhaving outliers, i.e. features that are matched incorrectly. Robustestimation allows discarding the outliers from the pose estimation.

A standard approach is disclosed in: M. A. Fischler and R. C. Bolles,“Random Sample Consensus: A Paradigm for Model Fitting with Applicationsto Image Analysis and Automated Cartography”, Communications of the ACM24: 381-395, June 1981. The standard approach is based on an algorithmthat performs the following two steps iteratively: a) the algorithmpicks randomly a sample of minimum number of features (also calledSample Set) needed to compute the parameters of a certain transformationmodel. This transformation can generally be described using a matrix;e.g. one can use 4 points in case the pose is computed via a homographymatrix estimation, one can use 5 points in case the pose is computed viaan essential matrix estimation, etc.; and b) it estimates thetransformation parameters and counts the number of matches (also calledConsensus Set) that verify them. To decide whether a matchm_(k)={r_(k),c_(k)} verifies the transformation parameters one can, forexample, transform the reference feature r_(k) from the reference imageinto the current image with this estimated transformation parameters andcompute the distance between the current feature c_(k) and thetransformed reference feature. A match is considered verifying thetransformation parameter set when the distance is smaller than a certainthreshold T_(m).

The algorithm performs a number N_(I) of iterations and searches for thebest transformation parameter set allowing the highest number of matchesverifying that parameter set (the highest cardinality of the ConsensusSet). If the number of matches corresponding to the best parameter setexceeds a certain threshold N_(m), the matches in the Consensus Setverifying the parameter set are considered as inliers (correct matches)and the other matches are considered as outliers (incorrect matches).The condition that the number of matches corresponding to the bestparameter set exceeds N_(m) is generally used to validate the success ofthe tracking initialization process. Only in the case of a successfultracking initialization process one can determine whether a match isinlier or outlier.

Limitations of the Standard Approaches:

Both the standard framework (performing Steps 1 to 7 as explained abovewith respect to FIG. 1) and the algorithm taking place in Step 6 andperforming the outlier rejection generally give good results. However,it happens that the reference images and the current images are acquireda) using different cameras (different sensors and image qualities); b)under different condition of the target (object dirty or slightlymodified); c) under different lighting conditions (the object isbrighter or darker in the images); and d) under very differentviewpoints, etc.

This results in a very weak matching process (Step 5) since thedescriptors of the features used cannot be discriminative in suchconditions. In fact, the difference of the environment, of the object tobe tracked or of the relative position affects the feature extractionand the feature description.

Also, it is common that the reference images are the result of anacquisition that was performed under very good or optimal conditions oreven instead of using real captures of the object to be tracked asreference images, one uses as reference images screenshots of therendering of the virtual version of the object. It is also common to usepoint clouds or geometries extracted from the real object (or scene) byvarious means (for example laser scanners coupled or not with camera or3D cameras or Time-of-Flight cameras) as reference features. Therefore,in general, much more details can be seen in the reference images (andthat cannot be seen in the live captures, i.e. in the current images)and there are usually much more reference features than currentfeatures. This often results in the following facts: The number of thereference features is very high.

This results in the matching process (Step 5) becoming inefficient andtoo slow for real-time or mobile applications. Only a small ratio of thereference and the current features are in common. Only a small ratio ofthe common features have close descriptors.

This results in that the outlier rejection algorithm (Step 6) does notwork or becomes also very slow because of the high number of outliers:in hard cases, it either fails or it needs a very high number N_(I) ofiterations in order to be able to select from the random sampling onecorrect set of inliers. Also, it happens when the threshold T_(m) usedto consider a match as inlier is too high, the algorithm picks the wronginliers' set.

Already Proposed Solutions:

One approach for improving the matching process is described in M.Grabner, H. Grabner, and H. Bischof, “Learning features for tracking”,Proceedings of IEEE Conference on Computer Vision and PatternRecognition (CVPR), Minneapolis, Minn., USA, June 2007., where theauthors learn feature classifiers and compute weights depending on thetemporal appearances and matches. They update the feature descriptorsover time. Their method is based on online feature ranking based onmeasures using the distributions of object and background pixels. Thefeature ranking mechanism is embedded in a tracking system thatadaptively selects the top-ranked discriminative features for tracking.The top-ranked features are the ones that best discriminate betweenobject and background classes.

Another approach for improving the outlier rejection algorithm is asfollows: In order to improve the result of the standard outlierrejection algorithm, it is possible to either rank or weigh theConsensus Set based on the matching strength or to give priorprobabilities to the Sample Set (like in O. Chum and J. Matas, “Matchingwith PROSAC—progressive sample consensus”, Proceedings of IEEEConference on Computer Vision and Pattern Recognition (CVPR), LosAlamitos, Calif., USA, June 2005) also based on the matching strength.The matching strength generally used is based on how good the similaritymeasure between the descriptors of two matched features is.

It would therefore beneficial to provide a method of determiningreference features for use in an optical object initialization trackingprocess and an object initialization tracking method making use ofreference features which are capable to reduce at least some of theabove mentioned limitations of standard approaches.

SUMMARY OF THE INVENTION

In a first aspect, there is provided a method of determining referencefeatures for use in an optical object initialization tracking process,the method comprising the following steps: a) capturing at least onecurrent image of a real environment or synthetically generated byrendering a virtual model of a real object to be tracked with at leastone camera and extracting current features from the at least one currentimage; b) providing reference features adapted for use in an opticalobject initialization tracking process; c) matching a plurality of thecurrent features with a plurality of the reference features; d)estimating at least one parameter associated with the current imagebased on a number of current and reference features which were matched,and determining for each of the reference features which were matchedwith one of the current features whether they were correctly orincorrectly matched; e) wherein the steps a) to d) are processediteratively multiple times, wherein in step a) of every respectiveiterative loop a respective new current image is captured by at leastone camera and steps a) to d) are processed with respect to therespective new current image; and f) determining at least one indicatorassociated to reference features which were correctly matched and/or toreference features which were incorrectly matched, wherein the at leastone indicator is determined depending on how often the respectivereference feature has been correctly matched or incorrectly matched,respectively.

In an embodiment of the invention, the indicator is a weight associatedto the reference features. For example, the weight of the referencefeatures is increased when the respective reference feature wascorrectly matched. According to another embodiment, additionally oralternatively, the weight of the reference features may be decreasedwhen the respective reference feature was incorrectly matched. Accordingto a further embodiment, if it is determined that one of the referencefeatures is not matched, its weight is not updated.

According to an embodiment, in step d) the method includes the step ofcomparing at least one property of the current image with at least oneproperty of a reference image, which provides the reference features,under consideration of the estimated at least one parameter, andverifying the estimated at least one parameter based on the comparison,and wherein the at least one indicator is updated if the estimated atleast one parameter is verified.

For example, when comparing at least one property of the current imagewith at least one property of the reference image under consideration ofthe estimated at least one parameter, a verification score value may bedetermined which is indicative of the comparison result, and theestimated at least one parameter is verified if the verification scorevalue is equal or beyond a verification threshold value.

According to an embodiment, the at least one parameter is, for example,indicative of a pose of the camera, a condition of the real environmentand/or a property of the real object to be tracked.

The method may further comprise the step of determining at least onefirst indicator and second indicator associated to each of the referencefeatures which were correctly matched and/or to each of the referencefeatures which were incorrectly matched, the first and second indicatorsbeing indicative of different respective conditions when capturingmultiple current images.

In a second aspect of the invention, there is provided an objectinitialization tracking method making use of reference features, themethod comprising the following steps: a) capturing at least one secondcurrent image with at least one camera and extracting current featuresfrom the at least one second current image; b) providing referencefeatures adapted for use in an optical object initialization trackingprocess, and providing at least one indicator associated to each of thereference features, wherein the indicator is determined according to themethod as set out above with respect to the first aspect of theinvention; c) the indicator is used for a selection or prioritization ofreference features which are to be matched with current features of theat least one second current image, and using the selected or prioritizedreference features in an object initialization tracking initializationprocess.

According to an embodiment, the method of the second aspect may comprisethe step of updating the at least one indicator associated to referencefeatures which were correctly matched and/or to reference features whichwere incorrectly matched with current features of the at least onesecond current image.

In a third aspect of the invention, there is provided an objectinitialization tracking method making use of reference features, themethod comprising the following steps: a) capturing at least one firstcurrent image of a real environment or synthetically generated byrendering a virtual model of a real object to be tracked with at leastone camera and extracting current features from the at least one firstcurrent image; b) providing reference features adapted for use in anoptical object initialization tracking process; c) matching a pluralityof the current features with a plurality of the reference features; d)determining at least one parameter associated with the first currentimage in an object initialization tracking process based on a number ofcurrent and reference features which were matched, and determining foreach of the reference features which were matched with one of thecurrent features whether they were correctly or incorrectly matched; e)updating at least one indicator associated to reference features whichwere correctly matched and/or to reference features which wereincorrectly matched; f) using the indicator for a selection orprioritization of reference features which are to be matched withcurrent features of at least one second current image captured by atleast one camera, and using the selected or prioritized referencefeatures in an object initialization tracking process with respect tothe second current image.

According to an embodiment, the indicator is a weight associated to eachof the reference features. For example, reference features with a weightabove a first threshold value are used in the matching process andfeatures with a weight below a second threshold value are not used inthe matching process

According to another embodiment of the invention, when estimating ordetermining the at least one parameter, a higher priority is applied toreference features that have a higher weight and a lower priority isapplied to reference features that have a lower weight, wherein thepriority is an input parameter when estimating or determining theparameter.

In another embodiment, the methods as described above may furthercomprise the steps of

providing at least one first indicator and second indicator associatedto each of the reference features which were correctly matched and/or toeach of the reference features which were incorrectly matched, the firstand second indicator being associated to different conditions, and usingat least one of the first and second indicators, or a combination of thefirst and second indicators, or switching between the first and secondindicators for the selection or prioritization of reference featuresaccording to the current estimated condition.

For example, the at least one indicator is additionally determinedaccording to a matching strength of the matched features.

In a further aspect of the invention, there is provided a computerprogram product adapted to be loaded into the internal memory of adigital computer system coupled with at least one camera, and comprisingsoftware code sections by means of which the steps according to any ofthe methods and embodiments as described above are performed when saidproduct is running on said computer system.

The invention will now be explained in more detail with reference to thefollowing Figures in which aspects of the invention are depictedaccording to various exemplary embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an exemplary flow diagram of a tracking initializationprocess according to a standard approach,

FIG. 2 shows a flow diagram of a process of determining referencefeatures (so-called training process) for use in an optical objectinitialization tracking process according to an embodiment of theinvention,

FIG. 3 shows a flow diagram of an initialization tracking process makinguse of reference features, indicators of which are updated in severaliterative loops (so-called online updating of indicators of referencefeatures during the initialization tracking process),

FIG. 4 shows in a schematic manner an exemplary current image capturedby a camera and depicting a real object, and an exemplary referenceimage depicting a reference object with respective extracted featureswhich are to be matched,

FIG. 5 shows in a schematic manner an exemplary reference image withextracted reference features which are weighted according to anembodiment of the invention.

DETAILED DESCRIPTION OF THE INVENTION

In the following, aspects and embodiments of the invention will beexplained with reference to the processes as depicted in FIGS. 2 and 3in connection with the schematic depictions of current and referenceimages according to FIGS. 4 and 5.

FIG. 2 shows a flow diagram of a process of determining referencefeatures for use in an optical object initialization tracking processaccording to an embodiment of the invention. The process may also becalled a “training process” for determining indicators of referencefeatures in a pre-processing stage of an object tracking initializationprocess, as will be apparent for the skilled person from the followingdescription. FIG. 2 depicts a possible implementation of such process.However, the skilled person will be aware that also other embodiments ofsuch processes may be applicable.

Reference features adapted for use in an optical object initializationtracking process may be provided according to steps 1 and 2. Forexample, such reference features are extracted from a set of referencedigital images. Schematic examples of reference features are shown inFIG. 4B, with a number of reference features RF extracted from areference image RI which depicts a reference object RO2. The referencefeatures RF can be points, a set of points (lines, segments, regions inthe image or simply a group of pixels), etc., as will be apparent to theperson skilled in the art. In addition to reference features orreference image, respectively, a reference pose and an object model maybe provided. These may be used in a later photometric pose verification,as explained in more detail below.

In a further step 2, descriptors (or classifiers) may be computed forevery reference feature RI extracted. These descriptors are calledreference descriptors.

In a further stage of the process a real object, such as real object RO1shown in FIG. 4A, is captured by at least one camera (not shown) in animage CI, which is referred to as current image. Therefore, currentimage CI shows real object RO1 as captured by the camera, wherein theobject RO1 is the object to be tracked in the later object trackinginitialization process. Alternatively to capturing a current image of areal environment, a current image may be generated synthetically byrendering a virtual model of a real object to be tracked.

In step 3, for every current image CI captured, features of a realobject, such as real object RO1 of FIG. 4A, are extracted. Thesefeatures are referred to as current features CF. In step 4, descriptors(or classifiers) may be computed for every current feature CF extracted.These descriptors are referred to as current descriptors.

In step 5, current features CF are matched with reference features RF.In the present implementation the reference and current descriptors areused for this purpose. Particularly, if the descriptors are relativelyclose in terms of a certain similarity measure, they are matched. Forexample, if every descriptor is written as a vector of numbers, whencomparing two descriptors, one can use the Euclidian distance betweentwo corresponding vectors as similarity measure.

In the following, generally, at least one parameter associated with thecurrent image is estimated based on a number of current and referencefeatures which were matched. According to an embodiment, such at leastone parameter is indicative of a pose of the camera, a condition of thereal environment and/or a property of the real object to be tracked.

In step 6, an outlier rejection algorithm is performed. The outlierrejection algorithm may be generally based on a robust pose estimation,as described above. A robust pose estimation may be performed in orderto determine and remove incorrect matches. This step is also referred toas outlier rejection. This is due to the fact that whatever descriptoror classifier used there is no way to avoid having outliers, i.e.features that are matched incorrectly. Robust pose estimation allowsdiscarding the outliers from the pose estimation. One approach that maybe used is disclosed in: M. A. Fischler and R. C. Bolles, “Random SampleConsensus: A Paradigm for Model Fitting with Applications to ImageAnalysis and Automated Cartography”, Communications of the ACM 24:381-395, June 1981, as described above in more detail. Therefore, instep 6 it is determined for each of the reference features which werematched with one of the current features whether they were correctly orincorrectly matched. Using the correct matches, the current pose of thecamera is computed (step 7).

Generally, these steps are processed iteratively multiple times, whereinin a first step of every respective iterative loop a respective newcurrent image is captured by at least one camera and the following stepsare processed with respect to the respective new current image.

As explained in the following, given reference features coming from oneor multiple reference images, laser scans coupled or not coupled withcamera images, or images from 3D cameras or Time-of-Flight cameras, thereference features are “trained” using a series of current images eithercaptured “offline” (i.e. before starting an object trackinginitialization process) or “online” (i.e. during an object trackinginitialization process) or synthetically generated by rendering avirtual model of the object to be tracked.

In this regard, at least one indicator associated to each of thereference features which were correctly matched and/or associated toeach of the reference features which were incorrectly matched isdetermined, wherein such indicator is determined depending on how oftenthe respective reference feature has been correctly matched orincorrectly matched, respectively. For example, the indicator is aweight associated to the reference features.

In the particular implementation as shown in FIG. 2, steps 3, 4, 5, 6and 7 are performed for every current image and additionally to thesesteps the reference features of both inlier and outlier matches providedby the outlier rejection algorithm are stored and a weight associated toevery reference feature is computed, the value of the weight dependingon how often that reference feature has participated in a successfultracking initialization. For example, starting from non-trained weights(i.e. all the reference features have the same weights), at everysuccessful initialization, the weight of every reference feature may beincreased when it is correctly matched and it may be decreased when itis wrongly matched. If a reference feature is not matched its weight isnot updated.

As described before, only in the case of a successful trackinginitialization process one can determine whether a match was correctlymatched (inlier) or incorrectly matched (outlier). Therefore, the choiceof the threshold N_(M) is quite important. If this threshold is set toolow, a match can be wrongly classified and the weights wrongly updated.If this threshold is set too high, the tracking initialization will berarely considered as successful and consequently the weights will berarely updated. In order to overcome this and in order to be able toupdate the weights as often as possible, according to an embodiment, thethreshold N_(M) is optionally set as low as possible and a photometricvalidation is performed, as shown in FIG. 2 in step 8. In the presentimplementation, the photometric validation consists in checking whetherthe pose determined with the inliers is correct or not using imageinformation instead of using the cardinality of the inliers. Forexample, in case of a planar target, one can use the transformationparameter determined by the robust pose estimation in order to build ahomography, warp the current image using the homography and perform acomparison between the reference image and the warped version of currentimage.

Generally, in step 8 at least one property of the current image may becompared with at least one property of a reference image underconsideration of the estimated pose of the camera. Particularly, theestimated pose of the camera is used for determining the transformationbetween the current image and the reference image. For performing thecomparison extracted features, pixels and/or any other properties (suchas brightness, etc.) of the reference and current images may becompared. In this regard, the skilled person is aware of how suchcomparison may be performed in a suitable way depending on theparticular situation. For example, when comparing at least one propertyof the current image with at least one property of the reference imageunder consideration of the estimated pose of the camera, a verificationscore value may be determined which is indicative of the comparisonresult, and the estimated pose of the camera is verified if theverification score value is equal or beyond a verification thresholdvalue.

In step 9, the estimated pose of the camera is verified based on thecomparison. In step 10, the weight for the respective reference featuresis updated according to the valid (correct) matches or non-valid(incorrect) matches, as determined in the robust pose estimationdescribed above, if the estimated pose of the camera is verified. Theprocess may then return to the step of capturing a new current image andprocessing the following steps 3 to 10 with respect to the respectivenew current image. If the process for training the reference features iscompleted or terminated, the trained weights for every reference featuremay be output and used for a subsequent tracking initialization process.

FIG. 5 shows in a schematic manner an exemplary reference image RIdepicting a reference object RO with extracted reference features RFwhich are weighted in a final stage of a training process according toan embodiment of the invention. FIGS. 5C and 5D show reference featuresRFH with high weights and reference features RFL with low weights,respectively, determined in several iterative loops of a trainingprocess, which may be output and used for a subsequent trackinginitialization process. The depicted magnitude of the reference featuresRFH, RFL is indicative of their respective weight.

Generally, having the trained weights of a previous training process, ina subsequent tracking initialization process at least one second orfurther current images are captured with at least one camera and currentfeatures from the respective further current images are extracted.Reference features are provided along with at least one respectiveindicator (e.g. weight) associated to each of the reference features,wherein the respective indicators were determined (“trained”) accordingto the described training process. The indicators may then be used for aselection or prioritization of reference features which are to bematched with current features of the further current images capturedduring the tracking initialization process. The selected or prioritizedreference features may then be used in the tracking initializationprocess.

According to an embodiment, the weights are then used for the selectionor prioritization of the features to be matched and to be used duringthe tracking initialization process (a) and for the robust poseestimation (b) as follows: (a) The reference features with very lowweight are not used in the matching process. This not only reduces thenumber of the reference features which results in a clear speed up ofthe matching process, but it also improves the results of the matchingprocess since only the best reference features that showed to allowsuccessful initializations are kept; (b) The robust pose estimation willgive priorities in its picking of the Sampling Set (cf. the approach forrobust pose estimation as described above) to the features that havehigh weights and it will give low priority to the features with lowweights in order to guide the “random” sampling procedure. It will alsouse the weights to evaluate the quality of the Consensus Set.

FIG. 3 shows a flow diagram of an initialization tracking process,wherein the weights of the reference features are updated in severaliterative loops in a so-called “online” updating during a initializationtracking process. The performed steps 1 to 10 as shown in FIG. 3 are thesame as described with reference to FIG. 2. As a difference to theprocess of FIG. 2 where the process for training the reference featuresis completed or terminated and the trained weights for every referencefeature are then output and used for a subsequent trackinginitialization process, according to FIG. 3 the training process isperformed during a tracking initialization process. Accordingly, in astep 11 after updating a respective indicator (weight) of referencefeatures in a respective loop, the updated weight is provided as aninput to the matching process in step 5 performed in the subsequentiterative loop. In this way, the tracking initialization process mayimprove after each iterative loop since the weight of reference featureswhich were correctly matched (valid matches) is gradually increased andthe weight of reference features which were incorrectly matched(non-valid matches) is gradually decreased.

Generally, in such online updating process, in each iterative loop theat least one indicator (e.g. weight) is updated, and the indicator isthen used for a selection or prioritization of the reference featureswhich are to be matched with current features of at least one furthercurrent image captured by at least one camera in a subsequent iterativeloop of the tracking initialization process. The selected or prioritizedreference features are then used in the object initialization trackingprocess with respect to the further current image, e.g. for determiningthe pose of the camera which captured the further current image.

According to an embodiment, the online process as described above withrespect to FIG. 3 may be combined with an offline process as describedwith respect to FIG. 2. Particularly, trained weights according to theprocess of FIG. 2 may be supplied as initial input parameters to theprocess according to FIG. 3, so that the process starts with trainedweights which may further be improved in the tracking initializationprocess.

Further Optional Improvements or Embodiments:

As said before, the weights may be determined according to the number oftimes that a feature was correctly matched in previous images. When afeature is extracted and correctly matched the weight increases, andwhen a feature is extracted and incorrectly matched the weightdecreases. Optionally, the weights can additionally be determinedaccording to the matching strength.

The feature training can be done either in a pre-processing stage (see apossible implementation in FIG. 2), i.e. with a set of current imagesacquired beforehand and used for that purpose. It can also be donewithout pre-processing stage and be performed online (see a possibleimplementation in FIG. 3). Or it can be done in a pre-processing stageand the weights continue to be updated online (see a possibleimplementation in FIG. 3).

The training can also be performed under several different conditionssuch as training with respect to bright lightening conditions, darklighting conditions, using a camera at a far distance from the object tobe tracked or close distance, different status of the object to betracked. Then, these different training results may be combined, orswitching between them may be performed according to the resultsobtained during the online tracking initialization process.

In another embodiment, the online or offline processes as describedabove may further comprise the steps of providing at least one firstindicator and second indicator associated to each of the referencefeatures which were correctly matched and/or to each of the referencefeatures which were incorrectly matched, the first and second indicatorbeing associated to different conditions (such as conditions of a realenvironment, e.g. brightness or light conditions etc.). The first andsecond indicators, or a combination of the first and second indicators,may then be used for the selection or prioritization of referencefeatures according to the current estimated condition. Alternatively,switching between the first and second indicators may be performed forthe selection or prioritization of reference features according to thecurrent estimated condition.

While this detailed description has set forth some embodiments of thepresent invention, the appended claims cover also other embodiments ofthe present invention which may differ from the described embodimentsaccording to various modifications and some aspects. Further, it is tobe understood that the above description of a possible implementation isintended to be illustrative and not restrictive. Moreover, in thisdisclosure the terms “first”, “second”, etc., are used merely as labels,and are not intended to impose numerical requirements on their objects.Other embodiments and modifications within the scope of the claims willbe apparent to those of skill in the art upon studying the abovedescription in connection with the drawings.

What is claimed is:
 1. A method of determining reference features foruse in an optical object initialization tracking process, said methodcomprising the following steps: a) capturing at least one current imagesynthetically generated by rendering a virtual model of a real object tobe tracked with at least one camera and extracting current features fromthe at least one current image; b) providing reference features adaptedfor use in an optical object initialization tracking process; c)matching a plurality of the current features with a plurality of thereference features; d) estimating at least one parameter associated withthe current image based on a number of current and reference featureswhich were matched, and determining for each of the reference featureswhich were matched with one of the current features whether they werecorrectly or incorrectly matched; e) wherein the steps a) to d) areprocessed iteratively multiple times, wherein in step a) of everyrespective iterative loop a respective new current image is captured byat least one camera and steps a) to d) are processed with respect to therespective new current image; and f) determining at least one indicatorassociated to reference features which were correctly matched and/or toreference features which were incorrectly matched, wherein the at leastone indicator is determined depending on how often the respectivereference feature has been correctly matched or incorrectly matched,respectively.
 2. The method of claim 1, wherein the indicator is aweight associated to the reference features.
 3. The method of claim 2,wherein the weight of the reference features is increased when therespective reference feature was correctly matched.
 4. The method ofclaim 2, wherein the weight of the reference features is decreased whenthe respective reference feature was incorrectly matched.
 5. The methodof claim 2, wherein if one of the reference features is not matched, itsweight is not updated.
 6. The method of claim 1, wherein in step d) themethod includes the step of comparing at least one property of thecurrent image with at least one property of a reference image, whichprovides the reference features, under consideration of the estimated atleast one parameter, and verifying the estimated at least one parameterbased on the comparison, and wherein the at least one indicator isupdated if the estimated at least one parameter is verified.
 7. Themethod of claim 6, wherein when comparing at least one property of thecurrent image with at least one property of the reference image underconsideration of the estimated at least one parameter, a verificationscore value is determined which is indicative of the comparison result,and the estimated at least one parameter is verified if the verificationscore value is equal or beyond a verification threshold value.
 8. Themethod of claim 7, further comprising the step of determining at leastone first indicator and second indicator associated to each of thereference features which were correctly matched and/or to each of thereference features which were incorrectly matched, the first and secondindicators being indicative of different respective conditions whencapturing multiple current images.
 9. An object initialization trackingmethod making use of reference features, said method comprising thefollowing steps: capturing at least one second current image with atleast one camera and extracting current features from the at least onesecond current image; providing reference features adapted for use in anoptical object initialization tracking process, and providing at leastone indicator associated to each of the reference features, wherein theindicator is determined by: a) capturing at least one current imagesynthetically generated by rendering a virtual model of a real object tobe tracked with at least one camera and extracting current features fromthe at least one current image; b) providing reference features adaptedfor use in an optical object initialization tracking process; c)matching a plurality of the current features with a plurality of thereference features; d) estimating at least one parameter associated withthe current image based on a number of current and reference featureswhich were matched, and determining for each of the reference featureswhich were matched with one of the current features whether they werecorrectly or incorrectly matched; e) wherein the steps a) to d) areprocessed iteratively multiple times, wherein in step a) of everyrespective iterative loop a respective new current image is captured byat least one camera and steps a) to d) are processed with respect to therespective new current image; and f) determining at least one indicatorassociated to reference features which were correctly matched and/or toreference features which were incorrectly matched, wherein the at leastone indicator is determined depending on how often the respectivereference feature has been correctly matched or incorrectly matched,respectively; and using the indicator for a selection or prioritizationof reference features which are to be matched with current features ofthe at least one second current image, and using the selected orprioritized reference features in an object initialization trackinginitialization process.
 10. The method of claim 9, further comprisingthe step of updating the at least one indicator associated to referencefeatures which were correctly matched and/or to reference features whichwere incorrectly matched with current features of the at least onesecond current image.
 11. An object initialization tracking methodmaking use of reference features, said method comprising the steps of:a) capturing at least one first current image synthetically generated byrendering a virtual model of a real object to be tracked with at leastone camera and extracting current features from the at least one firstcurrent image; b) providing reference features adapted for use in anoptical object initialization tracking process; c) matching a pluralityof the current features with a plurality of the reference features; d)determining at least one parameter associated with the first currentimage in an object initialization tracking process based on a number ofcurrent and reference features which were matched, and determining foreach of the reference features which were matched with one of thecurrent features whether they were correctly or incorrectly matched; e)updating at least one indicator associated to reference features whichwere correctly matched and/or to reference features which wereincorrectly matched; and f) using the indicator for a selection orprioritization of reference features which are to be matched withcurrent features of at least one second current image captured by atleast one camera, and using the selected or prioritized referencefeatures in an object initialization tracking process with respect tothe second current image.
 12. The method of claim 11, wherein theindicator is a weight associated to each of the reference features. 13.The method of claim 11, wherein the indicator is a weight associated toeach of the reference features, and reference features with a weightabove a first threshold value are used in the matching process andfeatures with a weight below a second threshold value are not used inthe matching process.
 14. The method of claim 11, wherein whenestimating or determining the parameter, a higher priority is applied toreference features that have a higher weight and a lower priority isapplied to reference features that have a lower weight, wherein thepriority is an input parameter when estimating or determining theparameter.
 15. The method of claim 11, further comprising providing atleast one first indicator and second indicator associated to each of thereference features which were correctly matched and/or to the referencefeatures which were incorrectly matched, the first and second indicatorbeing associated to different conditions, and using at least one of thefirst and second indicators, or a combination of the first and secondindicators, or switching between the first and second indicators for theselection or prioritization of reference features according to thecurrent estimated condition.
 16. The method of claim 15, wherein the atleast one indicator is additionally determined according to a matchingstrength of the matched features.
 17. The method of claim 11, whereinthe at least one parameter is indicative of at least one of thefollowing: a pose of the camera, a condition of the real environment, aproperty of the real object to be tracked.
 18. A non-transitorycomputer-readable medium comprising software code sections adapted to beloaded into the internal memory of a digital computer system coupledwith at least one camera, and being used to determine reference featuresfor use in an optical object initialization tracking process, and bymeans of which the following steps are performed: a) capturing at leastone current image synthetically generated by rendering a virtual modelof a real object to be tracked with at least one camera and extractingcurrent features from the at least one current image; b) providingreference features adapted for use in an optical object initializationtracking process; c) matching a plurality of the current features with aplurality of the reference features; d) estimating at least oneparameter associated with the current image based on a number of currentand reference features which were matched, and determining for each ofthe reference features which were matched with one of the currentfeatures whether they were correctly or incorrectly matched; e) whereinthe steps a) to d) are processed iteratively multiple times, wherein instep a) of every respective iterative loop a respective new currentimage is captured by at least one camera and steps a) to d) areprocessed with respect to the respective new current image; and f)determining at least one indicator associated to reference featureswhich were correctly matched and/or to reference features which wereincorrectly matched, wherein the at least one indicator is determineddepending on how often the respective reference feature has beencorrectly matched or incorrectly matched, respectively.